Since the inception of cDNA microarray technology [1] as a high throughput method to gain information about gene functions and characteristics of biological samples, many applications of the technology have been reported [2-10]. With the improvement of the technology, including fabrication, fluorescent labeling, hybridization, and detection, many computer software packages for extracting signals arising from tagged mRNA hybridized to arrayed cDNA locations have been designed and applied in various experiments [11-12]. As reported in [11], a target detection procedure has been implemented that utilizes manually specified target arrays, extracts the background via the image histogram, predicts target shape by mathematical morphology, and then evaluates the intensities from each cDNA location and its corresponding ratio quantity.
While most software packages are satisfactory for routine image analysis and the extraction of information regarding phenomena with highly expressed genes, the desire to discover subtle effects via microarray experiments will ultimately drive experiments towards the limit of the technology [13], with less starting mRNA and/or more weakly expressed genes. Weak signals and their interaction with background fluorescent noise are most problematic. Problems include the nonlinear trend in expression scatter plots, fishtailing at lower signal range, low measurement quality of expression levels due to uneven local background, and small cDNA-deposition areas. These artifacts, or sources of uncertainty, creep into higher-level statistical data analyses, such as clustering and classification, raising concerns about their validity.
Numerous remedies have been proposed, such as carefully designed experiments in which duplications are used to minimize the uncertainty [14]. However, given the scarcity of certain biological samples, large duplications of experiments are often impractical. Consequently, generating cDNA microarrays has posed challenges.